From c2a51bd2cc7c4148d9444e7377af44b2f6c264ba Mon Sep 17 00:00:00 2001 From: Gunes Bayir Date: Thu, 28 Sep 2023 10:30:18 +0100 Subject: Optimize CL and Neon Winograd tests Several test optimizations have been introduced into Winograd tests for Gpu and Cpu backends. The testing strategy has been detailed as a comment header in the test design files. In summary - Very large shapes in the nightly are made smaller - If the underlying kernel is the same for different data types, we only need to stress some key aspects of the kernels (e.g. read/write lengths in case of fp32/fp16). - In case the underlying kernel is the same (OpenCL), Fp16 is tested on a subset of the shapes - In Cpu, there is no need to test every combination for both NCHW and NHWC as we just permute the inputs and use NHWC kernels anyways - All activations does not need to be tested for each and every shape Resolves: COMPMID-6464 Change-Id: Ie25fded85c65b9c7386dc21b23f9b695b1e77b07 Signed-off-by: Gunes Bayir Reviewed-on: https://review.mlplatform.org/c/ml/ComputeLibrary/+/10393 Reviewed-by: Jakub Sujak Comments-Addressed: Arm Jenkins Tested-by: Arm Jenkins Benchmark: Arm Jenkins --- tests/validation/NEON/ConvolutionLayer.cpp | 451 ++++++++++++++++++++--------- 1 file changed, 319 insertions(+), 132 deletions(-) (limited to 'tests/validation/NEON/ConvolutionLayer.cpp') diff --git a/tests/validation/NEON/ConvolutionLayer.cpp b/tests/validation/NEON/ConvolutionLayer.cpp index 06fe9f7803..2f66100fb6 100644 --- a/tests/validation/NEON/ConvolutionLayer.cpp +++ b/tests/validation/NEON/ConvolutionLayer.cpp @@ -28,15 +28,16 @@ #include "arm_compute/runtime/NEON/functions/NEWinogradConvolutionLayer.h" #include "arm_compute/runtime/Tensor.h" #include "arm_compute/runtime/TensorAllocator.h" + +#include "src/core/CPP/Validate.h" #include "src/core/helpers/MemoryHelpers.h" #include "src/cpu/operators/CpuGemmConv2d.h" #include "src/cpu/operators/CpuGemmDirectConv2d.h" #include "src/cpu/operators/CpuWinogradConv2d.h" + #include "tests/NEON/Accessor.h" -#include "tests/PaddingCalculator.h" #include "tests/datasets/LargeConvolutionLayerDataset.h" #include "tests/datasets/SmallConvolutionLayerDataset.h" -#include "tests/datasets/TinyConvolutionLayerDataset.h" #include "tests/framework/Asserts.h" #include "tests/framework/Macros.h" #include "tests/framework/datasets/Datasets.h" @@ -50,6 +51,8 @@ namespace test { namespace validation { +using framework::dataset::make; + namespace detail { template <> @@ -85,13 +88,13 @@ constexpr float tolerance_num = 0.07f; #ifdef ARM_COMPUTE_ENABLE_SME // TODO(COMPMID-6011): SME kernels and the reference model use different rounding mode. // Temporarily increase the tolerance for quantized data. -constexpr AbsoluteTolerance tolerance_qasymm8(1.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ -#else // ARM_COMPUTE_ENABLE_SME -constexpr AbsoluteTolerance tolerance_qasymm8(0.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ -#endif // ARM_COMPUTE_ENABLE_SME +constexpr AbsoluteTolerance tolerance_qasymm8(1.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ +#else // ARM_COMPUTE_ENABLE_SME +constexpr AbsoluteTolerance tolerance_qasymm8(0.0); /**< Tolerance value for comparing reference's output against implementation's output for quantized data types */ +#endif // ARM_COMPUTE_ENABLE_SME /** CNN data types */ -const auto CNNDataTypes = framework::dataset::make("DataType", +const auto CNNDataTypes = make("DataType", { #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC DataType::F16, @@ -99,14 +102,36 @@ const auto CNNDataTypes = framework::dataset::make("DataType", DataType::F32, DataType::QASYMM8, }); -const auto ActivationFunctionsDataset = framework::dataset::make("ActivationInfo", +const auto ActivationFunctionsDataset = make("ActivationInfo", { ActivationLayerInfo(), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f) }); -const auto QuantizationData = framework::dataset::make("QuantizationInfo", +const auto ActivationFunctionsDatasetNightly = make("ActivationInfo", +{ + ActivationLayerInfo(), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::BOUNDED_RELU, 0.5f), + + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LU_BOUNDED_RELU, 0.5f, -0.5f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LEAKY_RELU, 0.1f), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SOFT_RELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ELU), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::ABS), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LOGISTIC), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::TANH), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SQUARE), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::SWISH), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::HARD_SWISH), + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::LINEAR, 2.f, 1.f), +#ifdef __aarch64__ + ActivationLayerInfo(ActivationLayerInfo::ActivationFunction::GELU), +#endif // __aarch64__ +}); + +const auto QuantizationData = make("QuantizationInfo", { QuantizationInfo(0.5f, 10), QuantizationInfo(0.3f, 3), @@ -121,32 +146,32 @@ TEST_SUITE(ConvolutionLayer) // *INDENT-OFF* // clang-format off DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32), + make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F32), TensorInfo(TensorShape(23U, 27U, 32U, 4U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 2U, 1U), 1, DataType::F32), TensorInfo(TensorShape(33U, 27U, 7U, 4U), 1, DataType::F32) }), - framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32), + make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F32), TensorInfo(TensorShape(5U, 5U, 32U, 21U), 1, DataType::F32), TensorInfo(TensorShape(3U, 3U, 5U, 21U), 1, DataType::F32), TensorInfo(TensorShape(5U, 5U, 7U, 16U), 1, DataType::F16) })), - framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32), + make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32), TensorInfo(TensorShape(19U, 23U, 21U, 4U), 1, DataType::F32), TensorInfo(TensorShape(11U, 25U, 21U), 1, DataType::F32), TensorInfo(TensorShape(11U, 12U, 16U, 4U), 1, DataType::F32) })), - framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), + make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), PadStrideInfo(1, 1, 0, 0), PadStrideInfo(2, 1, 0, 0), PadStrideInfo(3, 2, 1, 0) })), - framework::dataset::make("FastMath", { true, + make("FastMath", { true, true, false, false })), - framework::dataset::make("Expected", { ConvolutionMethod::WINOGRAD, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), + make("Expected", { ConvolutionMethod::WINOGRAD, ConvolutionMethod::WINOGRAD, ConvolutionMethod::GEMM, ConvolutionMethod::GEMM })), input_info, weights_info, output_info, conv_info, fast_math, expected) { ConvolutionMethod is_valid = NEConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true), @@ -158,6 +183,14 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z // *INDENT-ON* TEST_SUITE_END() // ConvolutionLayer +/* + Testing Strategy of Neon Winograd: + - There is no need to thoroughly test nchw cases because winograd kernels accept + nhwc and the tensors are permuted before and after if they're nchw. + - Except relu and bounded relu, testing activations for a single input + combination is enough because activation is not fused into winograd and called + separately. +*/ TEST_SUITE(WinogradLayer) template using NEWinogradConvolutionLayerFixture = WinogradConvolutionLayerFastMathValidationFixture; @@ -269,38 +302,148 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) } } +DATA_TEST_CASE(SupportedKernels, framework::DatasetMode::ALL, zip( + make("WeightsInfo", +{ + // Shapes are always in NCHW format. When layout is NHWC, the shape is permuted + + // Fp32, NCHW/NHWC (layout does not matter as it's ) + // 3x1, 1x3, 3x3 --> all TRUE + TensorInfo(TensorShape(3U, 3U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(1U, 3U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // 5x1, 1x5, 5x5 --> all TRUE + TensorInfo(TensorShape(5U, 5U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 5U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // 7x1, 1x7, 7x7 + // --> all FALSE + TensorInfo(TensorShape(7U, 7U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 7U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(7U, 1U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + + // unsupported kernel sizes + TensorInfo(TensorShape(2U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 2U, 2U, 8U), 1, DataType::F32, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 6U, 2U, 8U), 1, DataType::F32, DataLayout::NCHW), + + // Fp16 + TensorInfo(TensorShape(3U, 3U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(1U, 3U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 1U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + + // 5x1, 1x5, 5x5 --> all TRUE + TensorInfo(TensorShape(5U, 5U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 5U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 1U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + + // 7x1, 1x7, 7x7 + // --> all FALSE + TensorInfo(TensorShape(7U, 7U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + TensorInfo(TensorShape(1U, 7U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(7U, 1U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + + // unsupported kernel sizes + TensorInfo(TensorShape(2U, 2U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(5U, 2U, 2U, 8U), 1, DataType::F16, DataLayout::NHWC), + TensorInfo(TensorShape(3U, 6U, 2U, 8U), 1, DataType::F16, DataLayout::NCHW), + +}), +make("Expected", +{ + // fp32 + true, true, true, // 3x3, 1x3, 3x1 + true, true, true, // 5x5, 1x5, 5x1 + false, true, true, // 7x7, 1x7, 7x1 + false, false, false, // random unsupported kernels + + // fp16 + true, false, false, // 3x3, 1x3, 3x1 + false, false, false, // 5x5, 1x5, 5x1 + false, false, false, // 7x7, 1x7, 7x1 + false, false, false, // random unsupported kernels +})), +weights_info_const, expected_const) +{ + DataType data_type = weights_info_const.data_type(); + DataLayout data_layout = weights_info_const.data_layout(); + + TensorInfo input_info = TensorInfo(TensorShape(17U, 31U, 2U), 1, data_type); + TensorInfo bias_info = TensorInfo(TensorShape(8U), 1, data_type); + TensorInfo weights_info = weights_info_const; + + if(data_layout == DataLayout::NHWC) + { + // Convert to NHWC + PermutationVector perm = PermutationVector(2U, 0U, 1U); + + TensorShape input_shape = input_info.tensor_shape(); + TensorShape weights_shape = weights_info.tensor_shape(); + permute(input_shape, perm); + permute(weights_shape, perm); + + input_info.set_tensor_shape(input_shape); + weights_info.set_tensor_shape(weights_shape); + + input_info.set_data_layout(data_layout); + weights_info.set_data_layout(data_layout); + bias_info.set_data_layout(data_layout); + } + + PadStrideInfo conv_info(1, 1, 0, 0); + + TensorShape output_shape = compute_deep_convolution_shape(input_info, weights_info, conv_info); + TensorInfo output_info = TensorInfo(output_shape, 1, data_type, data_layout); + + Status status = NEWinogradConvolutionLayer::validate( + &input_info, + &weights_info, + &bias_info, + &output_info, + conv_info, + ActivationLayerInfo(), + true /* fast math */); + + Status fp16_supported = ::arm_compute::error_on_unsupported_cpu_fp16("N/A", "N/A", 0, &input_info); + bool expected = expected_const && static_cast(fp16_supported); + + ARM_COMPUTE_EXPECT(bool(status) == expected, framework::LogLevel::ERRORS); +} + TEST_SUITE(FP32) TEST_SUITE(Conv1x3) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunMixedDataLayout, NEWinogradConvolutionLayerMixedDataLayoutFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(combine(combine(combine(combine(combine( - framework::dataset::make("Input", TensorShape(8U, 8U, 32U)), - framework::dataset::make("Weight", TensorShape(1U, 3U, 32U, 1U))), - framework::dataset::make("Bias", TensorShape(1U))), - framework::dataset::make("Output", TensorShape(8U, 6U, 1U))), - framework::dataset::make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0))), - framework::dataset::make("Dilation", Size2D(1U, 1U))), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine( + make("Input", TensorShape(8U, 8U, 32U)), + make("Weight", TensorShape(1U, 3U, 32U, 1U)), + make("Bias", TensorShape(1U)), + make("Output", TensorShape(8U, 6U, 1U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x3Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -310,19 +453,19 @@ TEST_SUITE_END() // Conv1x3 TEST_SUITE(Conv3x1) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer3x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -332,19 +475,19 @@ TEST_SUITE_END() // Conv3x1 TEST_SUITE(Conv1x5) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer1x5Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -354,19 +497,19 @@ TEST_SUITE_END() // Conv1x5 TEST_SUITE(Conv5x1) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -376,10 +519,10 @@ TEST_SUITE_END() // Conv5x1 TEST_SUITE(Conv7x1) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer7x1Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); @@ -387,9 +530,9 @@ FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, frame FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + make("DataType", { DataType::F32 })), + make("ActivationInfo", { ActivationLayerInfo() })), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -398,20 +541,20 @@ TEST_SUITE_END() // Conv7x1 TEST_SUITE(Conv1x7) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer1x7Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer7x1Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_1xN_f32); @@ -420,20 +563,40 @@ TEST_SUITE_END() // Conv1x7 TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + +{ + // Validate output + validate(Accessor(_target), _reference, abs_tolerance_f32); +} +/// It's enough to run the activations for a single weight/input combination and data type because +/// activation function is called on top of the winograd output as a separate operator +/// TODO: Enable after COMPMID-6573 is resolved +FIXTURE_DATA_TEST_CASE(RunActivations, NEWinogradConvolutionLayerFixture, framework::DatasetMode::DISABLED, + combine( + make("Input", TensorShape(3U, 3U, 32U)), + make("Weight", TensorShape(3U, 3U, 32U, 4U)), + make("Bias", TensorShape(4U)), + make("Output", TensorShape(1U, 1U, 4U)), + make("PadStrideInfo", PadStrideInfo(1, 1, 0, 0)), + make("Dilation", Size2D(1U, 1U)), + make("DataType", { DataType::F32 }), + ActivationFunctionsDatasetNightly, + make("DataLayout", { DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } + FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output @@ -444,20 +607,20 @@ TEST_SUITE_END() // Conv3x3 TEST_SUITE(Conv5x5) FIXTURE_DATA_TEST_CASE(RunSmall, NEWinogradConvolutionLayerFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); } FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer5x5Dataset(), + make("DataType", { DataType::F32 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output @@ -467,12 +630,12 @@ FIXTURE_DATA_TEST_CASE(RunLarge, NEWinogradConvolutionLayerFixture, frame TEST_SUITE_END() // Conv5x5 FIXTURE_DATA_TEST_CASE(RunSmallNoBias, NEWinogradConvolutionLayerNoBiasFixture, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(framework::dataset::concat(datasets::SmallWinogradConvolutionLayer3x3Dataset(), - datasets::SmallWinogradConvolutionLayer5x5Dataset()), - framework::dataset::make("DataType", { DataType::F32 })), - ActivationFunctionsDataset), - - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(framework::dataset::concat( + datasets::SmallWinogradConvolutionLayer3x3Dataset(), + datasets::SmallWinogradConvolutionLayer5x5Dataset()), + make("DataType", { DataType::F32 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output validate(Accessor(_target), _reference, abs_tolerance_f32); @@ -484,24 +647,26 @@ TEST_SUITE_END() // FP32 TEST_SUITE(FP16) using CLWinogradConvolutionLayerFastMathFixture16 = WinogradConvolutionLayerFastMathValidationFixture; -DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(zip(zip(zip( - framework::dataset::make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F16), - TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F16) - }), - framework::dataset::make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F16), - TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F16) - })), - framework::dataset::make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32), - TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F16) - })), - framework::dataset::make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), - PadStrideInfo(1, 1, 0, 0) - })), - framework::dataset::make("FastMath", { false, // case fp16 and fast_math False then disable Winograd - true // case fp16 and fast_math True then enable Winograd - })), - framework::dataset::make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD })), - input_info, weights_info, output_info, conv_info, fast_math, expected) +DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip( + make("InputInfo", { TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F16), + TensorInfo(TensorShape(18U, 18U, 32U), 1, DataType::F16) + }), + make("WeightsInfo", { TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F16), + TensorInfo(TensorShape(3U, 3U, 32U, 21U), 1, DataType::F16) + }), + make("OutputInfo", { TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F32), + TensorInfo(TensorShape(16U, 16U, 21U), 1, DataType::F16) + }), + make("ConvInfo", { PadStrideInfo(1, 1, 0, 0), + PadStrideInfo(1, 1, 0, 0) + }), + make("FastMath", +{ + false, // case fp16 and fast_math False then disable Winograd + true // case fp16 and fast_math True then enable Winograd +}), +make("Expected", { ConvolutionMethod::GEMM, ConvolutionMethod::WINOGRAD })), +input_info, weights_info, output_info, conv_info, fast_math, expected) { ConvolutionMethod is_valid = NEConvolutionLayer::get_convolution_method(&input_info.clone()->set_is_resizable(true), &weights_info.clone()->set_is_resizable(true), @@ -511,10 +676,10 @@ DATA_TEST_CASE(ValidateConvolutionMethod, framework::DatasetMode::ALL, zip(zip(z TEST_SUITE(Conv3x3) FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::PRECOMMIT, - combine(combine(combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::SmallWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F16 }), + ActivationFunctionsDataset, + make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) { // Validate output @@ -522,10 +687,10 @@ FIXTURE_DATA_TEST_CASE(RunSmall, CLWinogradConvolutionLayerFastMathFixture16, fr } FIXTURE_DATA_TEST_CASE(RunLarge, CLWinogradConvolutionLayerFastMathFixture16, framework::DatasetMode::NIGHTLY, - combine(combine(combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), - framework::dataset::make("DataType", { DataType::F16 })), - ActivationFunctionsDataset), - framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC }))) + combine(datasets::LargeWinogradConvolutionLayer3x3Dataset(), + make("DataType", { DataType::F16 }), + make("ActivationInfo", { ActivationLayerInfo() }), + make("DataLayout", { DataLayout::NHWC }))) { // Validate output @@ -968,7 +1133,9 @@ TEST_SUITE(Float) #if defined(ARM_COMPUTE_ENABLE_BF16) TEST_SUITE(BFLOAT16) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::BFLOAT16)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::BFLOAT16)), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), ActivationFunctionsDataset)) { // Validate output @@ -980,7 +1147,10 @@ TEST_SUITE_END() // BFLOAT16 #ifdef __ARM_FEATURE_FP16_VECTOR_ARITHMETIC TEST_SUITE(FP16) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F16)), framework::dataset::make("DataLayout", { DataLayout::NCHW })), ActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::F16)), + framework::dataset::make("DataLayout", { DataLayout::NCHW })), + ActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f16, tolerance_num, abs_tolerance_f16); @@ -990,7 +1160,9 @@ TEST_SUITE_END() // FP16 TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), ActivationFunctionsDataset)) { // Validate output @@ -1032,8 +1204,11 @@ const auto QuantizedActivationFunctionsDataset = framework::dataset::make("Activ TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::QASYMM8)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), - framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), QuantizedActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), + QuantizedActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); @@ -1059,8 +1234,11 @@ TEST_SUITE_END() // QASYMM8 TEST_SUITE(QASYMM8_SIGNED) FIXTURE_DATA_TEST_CASE(RunSmall, NEGEMMConvolutionLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), - framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })), QuantizedActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", { DataLayout::NCHW, DataLayout::NHWC })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })), + QuantizedActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); @@ -1214,7 +1392,10 @@ TEST_CASE(MultipleExecutionWithConfigure, framework::DatasetMode::ALL) TEST_SUITE(Float) TEST_SUITE(FP32) FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::F32)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), ActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::F32)), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + ActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, rel_tolerance_f32, 0.f, float(abs_tolerance_f32)); @@ -1238,8 +1419,11 @@ const auto QuantizedActivationFunctionsDataset = framework::dataset::make("Activ TEST_SUITE(Quantized) TEST_SUITE(QASYMM8) FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::QASYMM8)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), QuantizedActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8)), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(2.f / 255.f, 10) })), + QuantizedActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); @@ -1248,8 +1432,11 @@ TEST_SUITE_END() // QASYMM8 TEST_SUITE(QASYMM8_SIGNED) FIXTURE_DATA_TEST_CASE(RunSmall, NEDirectGEMMConv2dLayerQuantizedFixture, framework::DatasetMode::ALL, combine(combine(combine(combine(combine(datasets::SmallConvolutionLayerDataset(), - framework::dataset::make("ReshapeWeights", { true })), framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), framework::dataset::make("DataLayout", { DataLayout::NHWC })), - framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })), QuantizedActivationFunctionsDataset)) + framework::dataset::make("ReshapeWeights", { true })), + framework::dataset::make("DataType", DataType::QASYMM8_SIGNED)), + framework::dataset::make("DataLayout", { DataLayout::NHWC })), + framework::dataset::make("QuantizationInfo", { QuantizationInfo(0.01f, -10) })), + QuantizedActivationFunctionsDataset)) { // Validate output validate(Accessor(_target), _reference, tolerance_qasymm8); -- cgit v1.2.1